inital deployment1

app.py

@@ -8,30 +8,6 @@ import cv2
 import numpy as np
 
 # Function to process the video frame
-def process_frame(frame):
-    # Convert frame to grayscale (example processing step)
-    gray_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
-    return gray_frame
-
-# Function to capture video feed
-def video_stream():
-    cap = cv2.VideoCapture(0)  # Open webcam
-    while True:
-        ret, frame = cap.read()
-        if not ret:
-            break
-        processed_frame = process_frame(frame)  # Apply processing
-        yield processed_frame  # Return processed frame
-    cap.release()
-
-# Create the Gradio interface
-iface = gr.Interface(
-    fn=video_stream,
-    inputs=[],
-    outputs=gr.Video(label="Webcam Feed"),
-    live=True
-)
-
 
 
 # Load the pre-trained Real-Time LCM model
@@ -45,13 +21,25 @@ def generate_image(prompt: str):
     image = pipe(prompt, num_inference_steps=4).images[0]
     return image
 
-if __name__ == "__main__":
-    iface.launch()
-
-
-
-
-# Launch the Gradio app
-if __name__ == "__main__":
-    iface.launch(share=True)
-
+def process_frame(frame):
+    """Process each frame (convert to grayscale as an example)"""
+    frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+    return frame
+
+def video_stream(frame):
+    """Receives video from webcam and processes it"""
+    if frame is None:
+        return None
+    
+    frame = cv2.imdecode(np.frombuffer(frame, np.uint8), cv2.IMREAD_COLOR)
+    processed_frame = process_frame(frame)
+    
+    return processed_frame
+
+# Gradio Interface
+iface = gr.Interface(
+    fn=video_stream,
+    inputs=gr.Video(source="webcam", streaming=True),  # Connect local webcam
+    outputs=gr.Image(label="Processed Webcam Feed"),   # Display processed frames
+    live=True
+)

requirements.txt

@@ -7,3 +7,5 @@ safetensors
 xformers
 torchvision
 opencv-python-headless
+opencv-python
+numpy